Senior Data Scientist

Mastercard - O'Fallon, MO

Part-time - Mid Level

O'Fallon, MO

Credit Intermediation and Related Activities

About the position

As a Senior Data Scientist at Mastercard, you will be part of the Cyber and Intelligence Platform Data Science team, which is dedicated to developing advanced deep learning Artificial Intelligence (A.I.) and Machine Learning (M.L.) models. These models are crucial for enhancing the security and efficiency of Mastercard's authentication and authorization networks. Your role will involve creating production-ready models that are tailored to specific products, ensuring they meet the highest standards of performance and reliability. The team is also focused on automating the model creation process, which includes all steps from data extraction to delivery, with an emphasis on scalability, repeatability, resilience, and industrialization. In this position, you will collaborate closely with business owners to grasp their requirements and performance metrics related to data quality and model performance for customer-facing products. You will work with various data sources and storage systems, building processes and pipelines that yield cohesive datasets for analysis and modeling. Your responsibilities will include generating, maintaining, and optimizing data pipelines, overseeing their implementation, and exploring fraudulent patterns to enhance the performance of fraud detection models. Additionally, you will conduct tests on trained models to ensure their robustness and readiness for deployment. This role requires a highly motivated individual with strong problem-solving skills, capable of structuring and engineering data while evaluating and reporting on cutting-edge A.I. models. You will be part of a team that applies innovative cross-channel AI solutions across various industries, including fintech, investment banking, biotech, healthcare, and insurance, particularly in Fortune 500 companies.

Responsibilities

• Work closely with business owners to understand business requirements and performance metrics regarding data quality and model performance of customer-facing products.
• Work with multiple disparate sources of data and storage systems to build processes and pipelines for cohesive datasets for analysis and modeling.
• Generate, maintain, and optimize data pipelines for reliable data delivery.
• Oversee the implementation of data pipelines.
• Explore fraudulent patterns or trends for feature discovery to enhance fraud detection model performance.
• Run tests on trained models to ensure robustness and assess readiness for deployment.

Requirements

• Data engineering and data science experience.
• Experience building machine learning models, preferably graph neural networks.
• Knowledge of model optimization techniques and ability to work closely with data scientists to implement and optimize models within big data pipelines.
• Experience with deep learning frameworks such as TensorFlow, PyTorch, and Keras, with a strong grasp of data science and machine learning concepts.
• Proficiency in SQL and experience with database technologies like PostgreSQL, Hadoop, Netezza, and Spark.
• Good knowledge of Linux/Bash environment.
• Proficiency in Python and Pyspark.
• Good communication skills and a high degree of initiative.
• At least an undergraduate degree in Computer Science or a STEM-related field.

Nice-to-haves

• Experience building payment fraud detection models.
• Graduate degree in Computer Science, Data Science, Machine Learning, AI, or a related STEM field.
• Experience with data engineering in PySpark on petabyte-scale data.
• Ability to evaluate work for errors and implement methods to improve accuracy.
• Enjoy working with error-prone, messy, disparate, unstructured data.

Benefits

• Insurance (medical, prescription drug, dental, vision, disability, life insurance)
• Flexible spending account and health savings account
• Paid leaves including 16 weeks new parent leave and up to 20 paid days bereavement leave
• 10 annual paid sick days
• 10 or more annual paid vacation days based on level
• 5 personal days
• 10 annual paid U.S. observed holidays
• 401k with a best-in-class company match
• Deferred compensation for eligible roles
• Fitness reimbursement or on-site fitness facilities
• Eligibility for tuition reimbursement
• Gender-inclusive benefits
• And many more.